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neural_network/numpy_nn/.ipynb_checkpoints/nn-checkpoint.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8f3ff90a",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import nnfs\n",
"from nnfs.datasets import spiral_data\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "439ab0e4",
"metadata": {},
"outputs": [],
"source": [
"nnfs.init()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2bc1bb6c",
"metadata": {},
"outputs": [],
"source": [
"class Stark_Layer():\n",
" def __init__(self, in_features, out_features):\n",
" self.weights = np.random.randn(in_features, out_features) * 0.1\n",
" self.bias = np.zeros((1, out_features))\n",
" def forward(self, inputs):\n",
" self.output = np.dot(inputs, self.weights) + self.bias\n",
" \n",
"class Activation_Relu():\n",
" def forward(self, inputs):\n",
" self.output = np.maximum(0, inputs)\n",
" \n",
"class Activation_Softmax():\n",
" def forward(self, inputs):\n",
" exp_values = np.exp(inputs - np.max(inputs, axis=1, keepdims=True))\n",
" self.output = exp_values / np.sum(exp_values, axis=1, keepdims=True)\n",
" \n",
"class Loss():\n",
" def calculate(self, output, y):\n",
" sample_loss = self.forward(output, y)\n",
" data_loss = np.mean(sample_loss)\n",
" return data_loss\n",
"\"\"\"交叉熵损失函数\"\"\"\n",
"class CategoricalCrossEntropy(Loss):\n",
" def one_hot(self, y_true):\n",
" y_hot = np.zeros((len(y_true), max(y_true) + 1))\n",
" y_hot[range(len(y_true)), y_true] = 1\n",
" return y_hot\n",
" def forward(self, y_pred, y_true):\n",
" samples = len(y_pred)\n",
" y_pred_clipped = np.clip(y_pred, 1e-7, 1-1e-7)#截断，放置np.log溢出\n",
" if len(y_true.shape) == 1:#标签不是独热码，通过one_hot函数转化\n",
" y_true = self.one_hot(y_true)\n",
" confidences = np.sum(y_pred_clipped * y_true, axis=1)\n",
" negative_log_likehoods = -np.log(confidences)\n",
" return negative_log_likehoods\n",
"\"\"\"准确率\"\"\"\n",
"class Accuracy():\n",
" def forward(self, logits, y_true):\n",
" predictions = np.argmax(logits, axis=1)#从激活输出选出最大值对应的下标即使标签\n",
" if len(y_true.shape) == 2:#独热编码\n",
" y_true = np.max(y_true, axis=1)\n",
" accuracy = np.mean(predictions == y_true)\n",
" return accuracy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5f334df4",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
" 0.00000000e+00]\n",
" [-8.35815910e-04 -7.90404272e-04 -1.33452227e-03 4.65504505e-04\n",
" 4.56846210e-05]\n",
" [-2.39994470e-03 5.93469958e-05 -2.24808278e-03 2.03573116e-04\n",
" 6.10024377e-04]\n",
" ...\n",
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" -6.71350807e-02]\n",
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" -6.81458861e-02]]\n",
"[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
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"1.0990554\n",
"0.3433333333333333\n"
]
}
],
"source": [
"if \"__main__\" == __name__:\n",
" #数据（3，4）（batch_size, in_features）\n",
" X, y = spiral_data(100, 3)#每个类别100个数据，3个类别就是300\n",
" plt.scatter(X[:, 0], X[:, 1], c=y, cmap='brg')\n",
" plt.show()\n",
" layer1 = Stark_Layer(2, 5)\n",
" layer2 = Stark_Layer(5, 3)\n",
" layer1.forward(X)\n",
" print(layer1.output)\n",
" layer2.forward(layer1.output)\n",
" print(layer2.output)\n",
" act1 = Activation_Relu()\n",
" act1.forward(layer2.output)\n",
" print(act1.output)\n",
" act2 = Activation_Softmax()\n",
" act2.forward(layer2.output)\n",
" act2.output, np.sum(act2.output, axis=1)\n",
" loss = CategoricalCrossEntropy()\n",
" cost = loss.calculate(act2.output, y)\n",
" print(cost)\n",
" acc = Accuracy()\n",
" accuracy = acc.forward(act2.output, y)\n",
" print(accuracy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15290c5d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
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"nbformat_minor": 5
}